Evaluating Spatial Representativeness of Station Observations for Remotely Sensed Leaf Area Index Products

Abstract

Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-toarea consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.

Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize

“…Therefore, the spatial representativeness of the LAI field measurements was assessed based on the LAI inversions from the NDVI-LAI relationships for ZY-3 MUX data within a 30 × 30 m subpixel region. The spatial representativeness was evaluated using the relative absolute error (RAE) and the coefficient of sill (CS), according to Xu et al [79]. In this study, the thresholds of RAE and CS were 10% for the representativeness evaluation based on LAI.…”

Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

“…The high accuracy of the proposed SIP model across the whole wavelength (400 nm-2500 nm) in Figure 4 also shows it would be promising to retrieve the canopy structure and leaf optics at the same time by the joint use of hyperspectral and multi-angular observations. To improve the retrieval accuracy, the recent progress in the field of RT forwarding modeling, inversion and validation should be accounted for, such as the mixed pixel effect [40,41], the variation of the vegetation density [42][43][44], the topography effect [45][46][47][48], and the spatial-temporal constraints [49][50][51].…”

New Scheme for Validating Remote-Sensing Land Surface Temperature Products with Station Observations

“…Ground-based LST measurements from two types LST observation instruments with different field of view (FOV) were selected to discuss the scale mismatch implications for validation of remote sensing LST products in the study by Yu et al, and the validation results show that there is an extra 26.9% in the error >3 K range caused by the 41.5 FOV difference [20].Therefore, we must assess the spatial representativeness of station observations at a given spatial resolution to reliably validate remotely sensed LSTs. Recently, several methods have been used to assess the spatial representativeness of different land-surface parameters, such as the leaf area index [21], surface solar radiation [22], bidirectional reflectance distribution function (BRDF)/albedo [23], air temperature [24] and air quality [25], which are observed by ground stations. These methods are based on two factors: the point-to-area consistency and the spatial heterogeneity [21].…”

“…Recently, several methods have been used to assess the spatial representativeness of different land-surface parameters, such as the leaf area index [21], surface solar radiation [22], bidirectional reflectance distribution function (BRDF)/albedo [23], air temperature [24] and air quality [25], which are observed by ground stations. These methods are based on two factors: the point-to-area consistency and the spatial heterogeneity [21]. The point-to-area consistency indicator can be calculated through two methods.…”

“…A first-order statistical algorithm is an important spatial heterogeneity indicator, for example, using window-size analysis to assess the spatial variation of the landscape around a given station [30]. Spatial representativeness assessments have been widely implemented to validate satellite-albedo, evapotranspiration, and LAI products [21,23,26,31,32]. However, few representativeness assessments exist for station LST observations, which increases the uncertainty of the validation of LST products, particularly for simple T-based implementations, and hinders the application of station observations.…”